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Adaptation of a Haptic Robot in a 3T fMRI
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Published on: October 4, 2011

Writer adaptation with style transfer mapping.

Xu-Yao Zhang1, Cheng-Lin Liu

  • 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Beijing 100190, P.R. China. xyz@nlpr.ia.ac.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 18, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces style transfer mapping (STM) to adapt handwriting recognition systems to individual writers, significantly boosting accuracy. This novel approach enhances writer adaptation for personalized recognition systems.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Personalized handwriting recognition requires adapting general models to unique writer styles.
  • Writer-independent classifiers often struggle with individual variations in handwriting.

Purpose of the Study:

  • To propose a novel framework, style transfer mapping (STM), for effective writer adaptation in handwriting recognition.
  • To enhance the accuracy of writer-independent classifiers by projecting data into a style-free space.

Main Methods:

  • Developed a writer-specific, class-independent feature transformation (STM) with a closed-form solution.
  • Integrated STM with learning vector quantization (LVQ) and modified quadratic discriminant function (MQDF) classifiers.
  • Evaluated STM in supervised, unsupervised, and semi-supervised adaptation scenarios using the CASIA-OLHWDB database.

Main Results:

  • STM significantly improves classification accuracy for personalized handwriting recognition.
  • Semi-supervised adaptation demonstrated the best performance, followed by unsupervised adaptation.
  • Error reduction rates of 31.95% (LVQ) and 25.00% (MQDF) were achieved with semi-supervised adaptation on handwritten text.

Conclusions:

  • Style transfer mapping (STM) is an efficient and effective method for writer adaptation in handwriting recognition.
  • The proposed framework enhances the performance of state-of-the-art classifiers for large-category Chinese handwriting recognition.
  • STM offers flexibility, enabling adaptation with labeled or unlabeled data across various classification settings.